1
|
Huang P, Liu SS, Wang ZJ, Ding TT, Xu YQ. Deriving the predicted no effect concentrations of 35 pesticides by the QSAR-SSD method. CHEMOSPHERE 2022; 298:134303. [PMID: 35288184 DOI: 10.1016/j.chemosphere.2022.134303] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 06/14/2023]
Abstract
The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues may cause certain risks to the environment and human health, and reliable predicted no effect concentrations (PNEC) must be obtained when assessing environmental risks. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of the SSD model requires sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) with certain uncertainty. However, most chemicals could not collect sufficient species toxicity data, while some chemicals had sufficient species toxicity data but could not find suitable fitting functions, thus hindering the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model. Combined with QSAR and SSD methods, a new method of PNEC derivation was developed and successfully applied to the derivation of PNEC for 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with few toxicity data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10%, i. e, HC5 and HC10, respectively, were calculated by the optimal SSD model. The assessment factor used to determine the PNEC of the chemical based on the HC10 was derived from the quantitative correlation between HC10 and HC5 of pesticides found in this study. When the toxicity data are insufficient, it may be more appropriate to calculate the PNECs of chemicals using HC10 than using HC5.
Collapse
Affiliation(s)
- Peng Huang
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China
| | - Shu-Shen Liu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
| | - Ze-Jun Wang
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China
| | - Ting-Ting Ding
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China
| | - Ya-Qian Xu
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China
| |
Collapse
|
2
|
The Recolonization Concentration Concept: Using Avoidance Assays with Soil Organisms to Predict the Recolonization Potential of Contaminated Sites. TOXICS 2022; 10:toxics10030127. [PMID: 35324752 PMCID: PMC8950574 DOI: 10.3390/toxics10030127] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 11/26/2022]
Abstract
In this study the recolonization concentration concept for soil organisms is presented and validated. This concept is based on the empirically deduced avoidance–recolonization hypothesis, which shows a negative correlation between avoidance (ACx) and recolonization (RCx) (ACx = RC100−x) responses. The concept was validated in a two-step approach composed by (i) individual placement tests, to demonstrate the non-influence of individual placement in a dual chamber avoidance test and (ii) small scale gradient tests to demonstrate that the number of colonizers reaching a soil patch with a certain concentration is independent on their previous exposure to lower concentrations. Overall, data show that avoidance data can be used, when framed under the recolonization concentration concept, to evaluate the recolonization potential of contaminated sites. The recolonization concept is an important theoretical concept that when coupled with spatial modelling tools could be used to tackle the spatial and temporal recovery dynamics of contaminated soil.
Collapse
|
3
|
Pirotta E. A review of bioenergetic modelling for marine mammal populations. CONSERVATION PHYSIOLOGY 2022; 10:coac036. [PMID: 35754757 PMCID: PMC9215292 DOI: 10.1093/conphys/coac036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/07/2022] [Accepted: 06/15/2022] [Indexed: 05/16/2023]
Abstract
Bioenergetic models describe the processes through which animals acquire energy from resources in the environment and allocate it to different life history functions. They capture some of the fundamental mechanisms regulating individuals, populations and ecosystems and have thus been used in a wide variety of theoretical and applied contexts. Here, I review the development of bioenergetic models for marine mammals and their application to management and conservation. For these long-lived, wide-ranging species, bioenergetic approaches were initially used to assess the energy requirements and prey consumption of individuals and populations. Increasingly, models are developed to describe the dynamics of energy intake and allocation and predict how resulting body reserves, vital rates and population dynamics might change as external conditions vary. The building blocks required to develop such models include estimates of intake rate, maintenance costs, growth patterns, energy storage and the dynamics of gestation and lactation, as well as rules for prioritizing allocation. I describe how these components have been parameterized for marine mammals and highlight critical research gaps. Large variation exists among available analytical approaches, reflecting the large range of life histories, management needs and data availability across studies. Flexibility in modelling strategy has supported tailored applications to specific case studies but has resulted in limited generality. Despite the many empirical and theoretical uncertainties that remain, bioenergetic models can be used to predict individual and population responses to environmental change and other anthropogenic impacts, thus providing powerful tools to inform effective management and conservation.
Collapse
Affiliation(s)
- Enrico Pirotta
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews KY16 9LZ, UK. Tel: (+44) (0)1334 461 842.
| |
Collapse
|
4
|
Accolla C, Vaugeois M, Grimm V, Moore AP, Rueda-Cediel P, Schmolke A, Forbes VE. A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent-Based Population Models for Ecological Risk Assessment. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2021; 17:521-540. [PMID: 33124764 DOI: 10.1002/ieam.4362] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/15/2020] [Accepted: 10/30/2020] [Indexed: 06/11/2023]
Abstract
Population models can provide valuable tools for ecological risk assessment (ERA). A growing amount of work on model development and documentation is now available to guide modelers and risk assessors to address different ERA questions. However, there remain misconceptions about population models for ERA, and communication between regulators and modelers can still be hindered by a lack of clarity in the underlying formalism, implementation, and complexity of different model types. In particular, there is confusion about differences among types of models and the implications of including or ignoring interactions of organisms with each other and their environment. In this review, we provide an overview of the key features represented in population models of relevance for ERA, which include density dependence, spatial heterogeneity, external drivers, stochasticity, life-history traits, behavior, energetics, and how exposure and effects are integrated in the models. We differentiate 3 broadly defined population model types (unstructured, structured, and agent-based) and explain how they can represent these key features. Depending on the ERA context, some model features will be more important than others, and this can inform model type choice, how features are implemented, and possibly the collection of additional data. We show that nearly all features can be included irrespective of formalization, but some features are more or less easily incorporated in certain model types. We also analyze how the key features have been used in published population models implemented as unstructured, structured, and agent-based models. The overall aim of this review is to increase confidence and understanding by model users and evaluators when considering the potential and adequacy of population models for use in ERA. Integr Environ Assess Manag 2021;17:521-540. © 2020 SETAC.
Collapse
Affiliation(s)
- Chiara Accolla
- Department of Ecology, Evolution, and Behavior, College of Biological Sciences, University of Minnesota, St Paul, Minnesota, USA
| | - Maxime Vaugeois
- Department of Ecology, Evolution, and Behavior, College of Biological Sciences, University of Minnesota, St Paul, Minnesota, USA
| | - Volker Grimm
- Department of Ecological Modelling, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Adrian P Moore
- Department of Ecology, Evolution, and Behavior, College of Biological Sciences, University of Minnesota, St Paul, Minnesota, USA
| | - Pamela Rueda-Cediel
- Department of Ecology, Evolution, and Behavior, College of Biological Sciences, University of Minnesota, St Paul, Minnesota, USA
| | | | - Valery E Forbes
- Department of Ecology, Evolution, and Behavior, College of Biological Sciences, University of Minnesota, St Paul, Minnesota, USA
| |
Collapse
|
5
|
Li Y, Blazer VS, Iwanowicz LR, Schall MK, Smalling K, Tillitt DE, Wagner T. Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu✰. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
6
|
Accolla C, Vaugeois M, Rueda-Cediel P, Moore A, Marques GM, Marella P, Forbes VE. DEB-tox and Data Gaps: Consequences for individual-level outputs. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
7
|
Zhang L, Shen L, Qin S, Cui J, Liu Y. Quinolones antibiotics in the Baiyangdian Lake, China: Occurrence, distribution, predicted no-effect concentrations (PNECs) and ecological risks by three methods. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 256:113458. [PMID: 31706758 DOI: 10.1016/j.envpol.2019.113458] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/10/2019] [Accepted: 10/21/2019] [Indexed: 05/23/2023]
Abstract
The occurrence, distribution, and ecological risk of 10 quinolones (QNs) were investigated in the water and sediment samples from Baiyangdian Lake, China. The field samplings were conducted in April (dry season) and August (wet season) 2018, the results showed that QNs was extensively distributed in the Baiyangdian Lake. For the occurrence, Flumequine (FLU) and Ofloxacin (OFL) were the most detected QNs in Baiyangdian Lake. For the temporal variation, the sum concentration of QNs in water and sediment were ranged from 153 ng/L to 3093 ng/L and from 40.1 ng/g to 1475 ng/g in April, while ranged from 3.83 ng/L to 769 ng/L and from 20.3 ng/g to 373 ng/g in August. For the spatial variation, all of QNs exhibited significance difference in concentration at different sampling areas. Furthermore, PNEC plays an important role in ecological risk assessment, thus the PNECs of FLU and OFL were derived by assessment factors (AF), species sensitivity distribution (SSD), and AQUATOX model methods. The results showed that: PNECAFs, PNECSSDs, and PNECAQUATOXs were 18.7 μg/L, 196 μg/L, and 128 μg/L for FLU, respectively; and were 0.021 μg/L, 4.40 μg/L, and 3.00 μg/L for OFL, respectively. The PNECs for FLU and OFL derived by three approaches showed the rank of: PNECSSDs > PNECAQUATOXs > PNECAFs; while the risk quotients (RQs) followed the other rank of: RQSSDs < RQAQUATOXs < RQAFs. The results was indicated that the indirect ecological effects plays an important role in the derived PNECs for QNs, without considering the indirect ecological effects in natural ecosystem can lead to under-protective or over-protective PNECs (RQs) for chemicals. Therefore, AQUATOX model can be applied in deriving PNECs during the ecological risk assessment.
Collapse
Affiliation(s)
- Lulu Zhang
- College of Environment Science and Engineering, Hebei University of Science and Technology, 050000, Shijiazhuang, Hebei Province, China.
| | - Lina Shen
- College of Environment Science and Engineering, Hebei University of Science and Technology, 050000, Shijiazhuang, Hebei Province, China
| | - Shan Qin
- College of Environment Science and Engineering, Hebei University of Science and Technology, 050000, Shijiazhuang, Hebei Province, China
| | - Jiansheng Cui
- College of Environment Science and Engineering, Hebei University of Science and Technology, 050000, Shijiazhuang, Hebei Province, China.
| | - Yong Liu
- College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, 100871, Beijing, China.
| |
Collapse
|
8
|
Gredelj A, Barausse A, Grechi L, Palmeri L. Deriving predicted no-effect concentrations (PNECs) for emerging contaminants in the river Po, Italy, using three approaches: Assessment factor, species sensitivity distribution and AQUATOX ecosystem modelling. ENVIRONMENT INTERNATIONAL 2018; 119:66-78. [PMID: 29935425 DOI: 10.1016/j.envint.2018.06.017] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 06/07/2018] [Accepted: 06/13/2018] [Indexed: 06/08/2023]
Abstract
Over the past decades, per- and polyfluoroalkyl substances (PFASs) found in environmental matrices worldwide have raised concerns due to their toxicity, ubiquity and persistence. A widespread pollution of groundwater and surface waters caused by PFASs in Northern Italy has been recently discovered, becoming a major environmental issue, also because the exact risk for humans and nature posed by this contamination is unclear. Here, the Po River in Northern Italy was selected as a study area to assess the ecological risk posed by perfluoroalkyl acids (PFAAs), a class of PFASs, considering the noticeable concentration of various PFAAs detected in the Po waters over the past years. Moreover, the Po has a large environmental and socio-economic importance: it is the largest Italian river and drains a densely inhabited, intensely cultivated and heavily industrialized watershed. Predicted no-effect concentrations (PNECs) were derived using two regulated methodologies, assessment factors (AFs) and species sensitivity distribution (SSD), which rely on published ecotoxicological laboratory tests. Results were compared to those of a novel methodology using the mechanistic ecosystem model AQUATOX to compute PNECs in an ecologically-sound manner, i.e. considering physical, chemical, biological and ecological processes in the river. The model was used to quantify how the biomasses of the modelled taxa in the river food web deviated from natural conditions due to varying inputs of the chemicals. PNEC for each chemical was defined as the lowest chemical concentration causing a non-negligible yearly biomass loss for a simulated taxon with respect to a control simulation. The investigated PFAAs were Perfluorooctanoic acid (PFOA) and Perfluorooctanesulfonic acid (PFOS) as long-chained compounds, and Perfluorobutanoic acid (PFBA) and Perfluorobutanesulfonic acid (PFBS) as short-chained homologues. Two emerging contaminants, Linear Alkylbenzene Sulfonate (LAS) and triclosan, were also studied to assess the performance of the three methodologies for chemicals whose ecotoxicology and environmental fate are well-studied. The most precautionary approach was the use of AFs generally followed by SSD and then AQUATOX, except for PFOS, for which AQUATOX yielded a much lower PNEC compared to the other approaches since, unlike the other two methodologies, it explicitly simulates sublethal toxicity and indirect ecological effects. Our findings highlight that neglecting the role of ecological processes when extrapolating from laboratory tests to ecosystems can result in under-protective threshold concentrations for chemicals. Ecosystem models can complement existing laboratory-based methodologies, and the use of multiple methods for deriving PNECs can help to clarify uncertainty in ecological risk estimates.
Collapse
Affiliation(s)
- Andrea Gredelj
- Environmental Systems Analysis Lab (LASA) Research Group, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy.
| | - Alberto Barausse
- Environmental Systems Analysis Lab (LASA) Research Group, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy.
| | - Laura Grechi
- Environmental Systems Analysis Lab (LASA) Research Group, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy
| | - Luca Palmeri
- Environmental Systems Analysis Lab (LASA) Research Group, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy
| |
Collapse
|
9
|
Mintram KS, Brown AR, Maynard SK, Thorbek P, Tyler CR. Capturing ecology in modeling approaches applied to environmental risk assessment of endocrine active chemicals in fish. Crit Rev Toxicol 2017; 48:109-120. [PMID: 28929839 DOI: 10.1080/10408444.2017.1367756] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Endocrine active chemicals (EACs) are widespread in freshwater environments and both laboratory and field based studies have shown reproductive effects in fish at environmentally relevant exposures. Environmental risk assessment (ERA) seeks to protect wildlife populations and prospective assessments rely on extrapolation from individual-level effects established for laboratory fish species to populations of wild fish using arbitrary safety factors. Population susceptibility to chemical effects, however, depends on exposure risk, physiological susceptibility, and population resilience, each of which can differ widely between fish species. Population models have significant potential to address these shortfalls and to include individual variability relating to life-history traits, demographic and density-dependent vital rates, and behaviors which arise from inter-organism and organism-environment interactions. Confidence in population models has recently resulted in the EU Commission stating that results derived from reliable models may be considered when assessing the relevance of adverse effects of EACs at the population level. This review critically assesses the potential risks posed by EACs for fish populations, considers the ecological factors influencing these risks and explores the benefits and challenges of applying population modeling (including individual-based modeling) in ERA for EACs in fish. We conclude that population modeling offers a way forward for incorporating greater environmental relevance in assessing the risks of EACs for fishes and for identifying key risk factors through sensitivity analysis. Individual-based models (IBMs) allow for the incorporation of physiological and behavioral endpoints relevant to EAC exposure effects, thus capturing both direct and indirect population-level effects.
Collapse
Affiliation(s)
- Kate S Mintram
- a College of Life and Environmental Sciences , University of Exeter , Exeter , UK
| | - A Ross Brown
- a College of Life and Environmental Sciences , University of Exeter , Exeter , UK
| | - Samuel K Maynard
- b Syngenta, Jealott's Hill International Research Centre , Bracknell , Berkshire , UK
| | - Pernille Thorbek
- b Syngenta, Jealott's Hill International Research Centre , Bracknell , Berkshire , UK
| | - Charles R Tyler
- a College of Life and Environmental Sciences , University of Exeter , Exeter , UK
| |
Collapse
|
10
|
Predicting population viability of a monocarpic perennial dune thistle using individual-based models. Ecol Modell 2017. [DOI: 10.1016/j.ecolmodel.2017.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
11
|
Ockleford C, Adriaanse P, Berny P, Brock T, Duquesne S, Grilli S, Hernandez-Jerez AF, Bennekou SH, Klein M, Kuhl T, Laskowski R, Machera K, Pelkonen O, Pieper S, Stemmer M, Sundh I, Teodorovic I, Tiktak A, Topping CJ, Wolterink G, Craig P, de Jong F, Manachini B, Sousa P, Swarowsky K, Auteri D, Arena M, Rob S. Scientific Opinion addressing the state of the science on risk assessment of plant protection products for in-soil organisms. EFSA J 2017; 15:e04690. [PMID: 32625401 PMCID: PMC7009882 DOI: 10.2903/j.efsa.2017.4690] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Following a request from EFSA, the Panel on Plant Protection Products and their Residues developed an opinion on the science behind the risk assessment of plant protection products for in-soil organisms. The current risk assessment scheme is reviewed, taking into account new regulatory frameworks and scientific developments. Proposals are made for specific protection goals for in-soil organisms being key drivers for relevant ecosystem services in agricultural landscapes such as nutrient cycling, soil structure, pest control and biodiversity. Considering the time-scales and biological processes related to the dispersal of the majority of in-soil organisms compared to terrestrial non-target arthropods living above soil, the Panel proposes that in-soil environmental risk assessments are made at in- and off-field scale considering field boundary levels. A new testing strategy which takes into account the relevant exposure routes for in-soil organisms and the potential direct and indirect effects is proposed. In order to address species recovery and long-term impacts of PPPs, the use of population models is also proposed.
Collapse
|
12
|
Simple or complex: Relative impact of data availability and model purpose on the choice of model types for population viability analyses. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2015.11.022] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
13
|
Erickson RA, Eager EA, Stanton JC, Beston JA, Diffendorfer JE, Thogmartin WE. Assessing local population vulnerability with branching process models: an application to wind energy development. Ecosphere 2015. [DOI: 10.1890/es15-00103.1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
14
|
de los Santos CB, Neuparth T, Torres T, Martins I, Cunha I, Sheahan D, McGowan T, Santos MM. Ecological modelling and toxicity data coupled to assess population recovery of marine amphipod Gammarus locusta: Application to disturbance by chronic exposure to aniline. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2015; 163:60-70. [PMID: 25854699 DOI: 10.1016/j.aquatox.2015.03.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 03/19/2015] [Accepted: 03/21/2015] [Indexed: 06/04/2023]
Abstract
A population agent-based model of marine amphipod Gammarus locusta was designed and implemented as a basis for ecological risk assessment of chemical pollutants impairing life-history traits at the individual level. We further used the model to assess the toxic effects of aniline (a priority hazardous and noxious substance, HNS) on amphipod populations using empirically-built dose-response functions derived from a chronic bioassay that we previously performed with this species. We observed a significant toxicant-induced mortality and adverse effects in reproductive performance (reduction of newborn production) in G. locusta at the individual level. Coupling the population model with the toxicological data from the chronic bioassay allowed the projection of the ecological costs associated with exposure to aniline that might occur in wild populations. Model simulations with different scenarios indicated that even low level prolonged exposure to the HNS aniline can have significant long-term impacts on G. locusta population abundance, until the impacted population returns to undisturbed levels. This approach may be a useful complement in ecotoxicological studies of chemical pollution to transfer individual-collected data to ecological-relevant levels.
Collapse
Affiliation(s)
- Carmen B de los Santos
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal.
| | - Teresa Neuparth
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal
| | - Tiago Torres
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal
| | - Irene Martins
- School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey LL59 5AB, UK
| | - Isabel Cunha
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal
| | - Dave Sheahan
- Centre for Environment, Fisheries and Aquaculture Science, Lowestoft NR33 0HT, UK
| | - Tom McGowan
- Centre for Environment, Fisheries and Aquaculture Science, Lowestoft NR33 0HT, UK
| | - Miguel M Santos
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal; FCUP - Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal.
| |
Collapse
|
15
|
Lundström Belleza E, Brinkmann M, Preuss TG, Breitholtz M. Population-level effects in Amphiascus tenuiremis: contrasting matrix- and individual-based population models. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2014; 157:207-214. [PMID: 25456235 DOI: 10.1016/j.aquatox.2014.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Revised: 10/08/2014] [Accepted: 10/12/2014] [Indexed: 06/04/2023]
Abstract
Environmental risk assessment (ERA) is generally based on individual-level endpoints, even though protection goals in ERA intend higher biological levels. Population models have the potential to translate individual-level endpoints to population-level responses and range from simple demographic equations to highly complex individual based models (IBMs). The aims of the current study were to develop a matrix model (MM) with the structure and parameterization proposed in the draft OECD guideline "Harpacticoid copepod development and reproduction test with Amphiascus tenuiremis", and an IBM with the same data requirements. Experimental data from lindane exposure from validation studies of the OECD guideline was projected to the population level. Lindane does not only cause effects on survival and reproduction, but also on the time it takes to develop from larvae to adults. The two model approaches were contrasted in terms of their ability to properly project these effects on development. The MM projected smaller effects of the lindane treatments on population growth rate compared to the IBM since in its proposed structure, it did not include the delay in development explicitly. Population-level EC10 for population growth rate in the IBM was at the same level as the most sensitive individual-level endpoint, whereas the EC10 from the MM was not as sensitive. Based on these findings, our conclusion is that the IBM (or an improved MM) should be used for datasets including shifts in development, whereas the simpler MM is sufficient for datasets where only mortality and reproduction are affected, or as a screening tool in lower-tier population-level ERA.
Collapse
Affiliation(s)
- Elin Lundström Belleza
- Stockholm University, Department of Applied Environmental Science (ITM), Svante Arrhenius väg 8, S-106 91 Stockholm, Sweden
| | - Markus Brinkmann
- RWTH Aachen University, ABBt - Aachen Biology and Biotechnology, Institute for Environmental Research, Department of Ecosystem Analysis, Worringerweg 1, D-520 74 Aachen, Germany
| | - Thomas G Preuss
- RWTH Aachen University, ABBt - Aachen Biology and Biotechnology, Institute for Environmental Research, Chair of Environmental Biology and Chemodynamics, Worringerweg 1, D-520 74 Aachen, Germany
| | - Magnus Breitholtz
- Stockholm University, Department of Applied Environmental Science (ITM), Svante Arrhenius väg 8, S-106 91 Stockholm, Sweden.
| |
Collapse
|
16
|
Grimm V, Augusiak J, Focks A, Frank BM, Gabsi F, Johnston AS, Liu C, Martin BT, Meli M, Radchuk V, Thorbek P, Railsback SF. Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE. Ecol Modell 2014. [DOI: 10.1016/j.ecolmodel.2014.01.018] [Citation(s) in RCA: 95] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
17
|
Grimm V, Thorbek P. Population models for ecological risk assessment of chemicals: Short introduction and summary of a special issue. Ecol Modell 2014. [DOI: 10.1016/j.ecolmodel.2014.01.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|